A stereo approach for 3D plant modelling is presented. Using only a set of photographies, the method produces a dense 3D point cloud sampling the plant surface. Clustering automatically segments the plant structure in meaningful parts, which are classified in elements of interest as leaves and internodes. Measurements can be computed for each element, as area or surface normals.

This paper shows as the state of the art in structure from motion and multiple view stereo is able to produce accurate 3D models for specimens presenting sparse canopies. Three-dimensional triangular meshes are computed from a set of non-calibrated images, modeling a basil and an Ixora specimens and accurately representing their leaves and branches.